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                <div class="bbp-reply-header" id="post-271791">
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                   2009年6月3日 上午1:58
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                   回复：
                   <a class="bbp-topic-permalink" href="http://cos.name/cn/topic/15384/">
                    分析结果不一样
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                   10 楼
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                   jhfu
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                   普通会员
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                  <p>
                   1、程序语句
                   <br/>
                   MANOVA led hed net WITH arti com man
                   <br/>
                   /DISCRIM ALL ALPHA(1)
                   <br/>
                   /PRINT =SIGNIF(EIGEN DIM).
                   <br/>
                   2、输出结果* * * * * * * * * * * * * * * * * A n a l y s i s   o f   V a r i a n c e * * * * * * * * * * * * * * * * *
                   <br/>
                   The default error term in MANOVA has been changed from WITHIN CELLS to
                   <br/>
                   WITHIN+RESIDUAL.  Note that these are the same for all full factorial designs.
                   <br/>
                   * * * * * * * * * * * * * * * * * A n a l y s i s   o f   V a r i a n c e — Design   1 * * * * * * * * * * * * * * * * *
                   <br/>
                   EFFECT .. WITHIN CELLS Regression
                   <br/>
                   Multivariate Tests of Significance (S = 3, M = -1/2, N = 11 )
                  </p>
                  <p>
                   Test Name             Value        Approx. F       Hypoth. DF         Error DF        Sig. of F
                  </p>
                  <p>
                   Pillais               2.30495         28.74054             9.00            78.00             .000
                   <br/>
                   Hotellings       119.44882     300.83406           9.00            68.00             .000
                   <br/>
                   Wilks                  .00050        141.58046             9.00            58.56             .000
                   <br/>
                   Roys                   .99090
                   <br/>
                   – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –
                   <br/>
                   Eigenvalues and Canonical Correlations
                  </p>
                  <p>
                   Root No.       Eigenvalue           Pct.      Cum. Pct.     Canon Cor.        Sq. Cor
                  </p>
                  <p>
                   1        108.91116       91.17810       91.17810         .99544         .99090
                   <br/>
                   2          9.85364         8.24926         99.42736          .95282         .90787
                   <br/>
                   3           .68401          .57264           100.00000         .63732         .40618
                   <br/>
                   – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –
                   <br/>
                   Dimension Reduction Analysis
                  </p>
                  <p>
                   Roots              Wilks L.                F       Hypoth. DF         Error DF        Sig. of F
                  </p>
                  <p>
                   1 TO 3               .00050        141.58046             9.00            58.56             .000
                   <br/>
                   2 TO 3               .05471         40.94049             4.00            50.00             .000
                   <br/>
                   3 TO 3               .59382         17.78432             1.00            26.00             .000
                  </p>
                  <p>
                   – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –
                   <br/>
                   EFFECT .. WITHIN CELLS Regression (Cont.)
                   <br/>
                   Univariate F-tests with (3,26) D. F.
                  </p>
                  <p>
                   Variable       Sq. Mul. R     Adj. R-sq.     Hypoth. MS       Error MS              F      Sig. of F
                  </p>
                  <p>
                   led                .89994         .88839        8.69941         .11161       77.94711           .000
                   <br/>
                   hed                .98818         .98682        9.55243         .01318      724.68163           .000
                   <br/>
                   net                .77631         .75050        7.50434         .24950       30.07759           .000
                   <br/>
                   – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –
                   <br/>
                   Raw canonical coefficients for DEPENDENT variables
                   <br/>
                   Function No.
                   <br/>
                   Variable                  1                2                3
                  </p>
                  <p>
                   led                  .14887           .78575         -1.21198
                   <br/>
                   hed                  .97696          -.38311          -.15951
                   <br/>
                   net                 -.05201           .31163          1.46710
                   <br/>
                   – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –
                   <br/>
                   Standardized canonical coefficients for DEPENDENT variables
                   <br/>
                   Function No.
                   <br/>
                   Variable                  1                2                3
                  </p>
                  <p>
                   led                  .14887           .78575         -1.21198
                   <br/>
                   hed                  .97696          -.38311          -.15951
                   <br/>
                   net                 -.05201           .31163          1.46710
                   <br/>
                   – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –
                   <br/>
                   Correlations between DEPENDENT and canonical variables
                   <br/>
                   Function No.
                  </p>
                  <p>
                   Variable                  1                2                3
                  </p>
                  <p>
                   led                  .33252           .92484          -.18466
                   <br/>
                   hed                  .99329          -.10084           .05663
                   <br/>
                   net                  .38269           .75305           .53522
                   <br/>
                   – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –
                   <br/>
                   Variance in dependent variables explained by canonical variables
                  </p>
                  <p>
                   CAN. VAR.       Pct Var DEP      Cum Pct DEP      Pct Var COV      Cum Pct COV
                  </p>
                  <p>
                   1           41.45484         41.45484         41.07767         41.07767
                   <br/>
                   2           47.75277         89.20761         43.35307         84.43074
                   <br/>
                   3           10.79239        100.00000          4.38365         88.81440
                   <br/>
                   – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –
                   <br/>
                   Raw canonical coefficients for COVARIATES
                   <br/>
                   Function No.
                  </p>
                  <p>
                   COVARIATE                 1                2                3
                  </p>
                  <p>
                   arti                 .85751          -.91113         -1.98252
                   <br/>
                   com                  .01930          1.04627         -1.11428
                   <br/>
                   man                  .14539           .33714          2.83316
                   <br/>
                   – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –
                   <br/>
                   Standardized canonical coefficients for COVARIATES
                   <br/>
                   CAN. VAR.
                   <br/>
                   COVARIATE                 1                2                3
                  </p>
                  <p>
                   arti                 .85751          -.91113         -1.98252
                   <br/>
                   com                  .01930          1.04627         -1.11428
                   <br/>
                   man                  .14539           .33714          2.83316
                   <br/>
                   – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –
                   <br/>
                   Correlations between COVARIATES and canonical variables
                   <br/>
                   CAN. VAR.
                  </p>
                  <p>
                   Covariate                 1                2                3
                  </p>
                  <p>
                   arti                 .99696          -.06468          -.04346
                   <br/>
                   com                  .57103           .81123          -.12584
                   <br/>
                   man                  .92221           .27380           .27306
                   <br/>
                   – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –
                   <br/>
                   Variance in covariates explained by canonical variables
                  </p>
                  <p>
                   CAN. VAR.       Pct Var DEP      Cum Pct DEP      Pct Var COV      Cum Pct COV
                  </p>
                  <p>
                   1           71.69100         71.69100         72.34925         72.34925
                   <br/>
                   2           22.31042         94.00142         24.57460         96.92385
                   <br/>
                   3            1.24947         95.25089          3.07615        100.00000
                   <br/>
                   – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –
                   <br/>
                   Regression analysis for WITHIN CELLS error term
                   <br/>
                   — Individual Univariate .9500 confidence intervals
                   <br/>
                   Dependent variable .. led
                  </p>
                  <p>
                   COVARIATE               B           Beta      Std. Err.        t-Value      Sig. of t     Lower -95%     CL- Upper
                  </p>
                  <p>
                   arti         -.2857414421   -.2857414421         .14543       -1.96476           .060        -.58468         .01320
                   <br/>
                   com          1.0595057740   1.0595057740         .09483       11.17272           .000         .86458        1.25443
                   <br/>
                   man           .0117817825    .0117817825         .17723         .06648           .948        -.35252         .37608
                   <br/>
                   Dependent variable .. hed
                  </p>
                  <p>
                   COVARIATE               B           Beta      Std. Err.        t-Value      Sig. of t     Lower -95%     CL- Upper
                  </p>
                  <p>
                   arti          .8638568439    .8638568439         .04998       17.28381           .000         .76112         .96659
                   <br/>
                   com          -.1216608663   -.1216608663         .03259       -3.73309           .001        -.18865        -.05467
                   <br/>
                   man           .2136198867    .2136198867         .06091        3.50728           .002         .08842         .33882
                   <br/>
                   Dependent variable .. net
                  </p>
                  <p>
                   COVARIATE               B           Beta      Std. Err.        t-Value      Sig. of t     Lower -95%     CL- Upper
                  </p>
                  <p>
                   arti        -1.0033492783  -1.0033492783         .21745       -4.61422           .000       -1.45032        -.55638
                   <br/>
                   com           .3779792532    .3779792532         .14179        2.66584           .013         .08653         .66943
                   <br/>
                   man          1.2637082710   1.2637082710         .26499        4.76896           .000         .71902        1.80840
                   <br/>
                   – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –
                   <br/>
                   * * * * * * * * * * * * * * * * * A n a l y s i s   o f   V a r i a n c e — Design   1 * * * * * * * * * * * * * * * * *
                   <br/>
                   EFFECT .. CONSTANT
                   <br/>
                   Multivariate Tests of Significance (S = 1, M = 1/2, N = 11 )
                  </p>
                  <p>
                   Test Name             Value          Exact F       Hypoth. DF         Error DF        Sig. of F
                  </p>
                  <p>
                   Pillais                .00000           .00000             3.00            24.00            1.000
                   <br/>
                   Hotellings             .00000           .00000             3.00            24.00            1.000
                   <br/>
                   Wilks                 1.00000           .00000             3.00            24.00            1.000
                   <br/>
                   Roys                   .00000
                   <br/>
                   Note.. F statistics are exact.
                   <br/>
                   – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –
                   <br/>
                   Eigenvalues and Canonical Correlations
                  </p>
                  <p>
                   Root No.       Eigenvalue           Pct.      Cum. Pct.     Canon Cor.
                  </p>
                  <p>
                   1           .00000            INF      100.00000         .00000
                   <br/>
                   – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –
                   <br/>
                   EFFECT .. CONSTANT (Cont.)
                   <br/>
                   Univariate F-tests with (1,26) D. F.
                  </p>
                  <p>
                   Variable         Hypoth. SS         Error SS       Hypoth. MS         Error MS                F        Sig. of F
                  </p>
                  <p>
                   led                  .00000          2.90177           .00000           .11161           .00000            1.000
                   <br/>
                   hed                  .00000           .34272           .00000           .01318           .00000            1.000
                   <br/>
                   net                  .00000          6.48698           .00000           .24950           .                  .
                   <br/>
                   – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –
                   <br/>
                   EFFECT .. CONSTANT (Cont.)
                   <br/>
                   Raw discriminant function coefficients
                   <br/>
                   Function No.
                  </p>
                  <p>
                   Variable                  1
                  </p>
                  <p>
                   led                -3.15310
                   <br/>
                   hed                -4.40979
                   <br/>
                   net                  .00000
                   <br/>
                   – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –
                   <br/>
                   Standardized discriminant function coefficients
                   <br/>
                   Function No.
                  </p>
                  <p>
                   Variable                  1
                  </p>
                  <p>
                   led                -1.05337
                   <br/>
                   hed                 -.50629
                   <br/>
                   net                  .00000
                   <br/>
                   – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –
                   <br/>
                   Estimates of effects for canonical variables
                   <br/>
                   Canonical Variable
                  </p>
                  <p>
                   Parameter                1
                  </p>
                  <p>
                   1             .00000
                   <br/>
                   – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –
                   <br/>
                   Correlations between DEPENDENT and canonical variables
                   <br/>
                   Canonical Variable
                  </p>
                  <p>
                   Variable                  1
                  </p>
                  <p>
                   led                 -.87968
                   <br/>
                   hed                 -.14491
                   <br/>
                   net                 -.18912
                   <br/>
                   – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –
                  </p>
                 </div>
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                <div class="bbp-reply-header" id="post-271685">
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                  <span class="bbp-reply-post-date">
                   2009年6月1日 下午2:49
                  </span>
                  <span class="bbp-header">
                   回复：
                   <a class="bbp-topic-permalink" href="http://cos.name/cn/topic/15384/">
                    分析结果不一样
                   </a>
                  </span>
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                   8 楼
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                  <p>
                   我在两个软件上用的数据全部是经过R软件标准化的，也就是说，是经过R软件标准化后的数据再进入SPSS软件进行典型相关分析的，而且可以得到验证（这时SPSS输出的典型变量的标准化系数与非标准化系数是完全一样的）。
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                <div class="bbp-reply-header" id="post-271632">
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                  <span class="bbp-reply-post-date">
                   2009年6月1日 上午3:57
                  </span>
                  <span class="bbp-header">
                   回复：
                   <a class="bbp-topic-permalink" href="http://cos.name/cn/topic/15384/">
                    分析结果不一样
                   </a>
                  </span>
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                   6 楼
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                  <p>
                   不是这个原因。SPSS在做典型相关分析时同时给出数据标准化和数据未标准化两种情况下的综合变量（典型变量）系数。刚才我又试了试，把数据标准化后，再分别用spss和r软件做，两个软件给出的结果还是不一样。
                  </p>
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                <div class="bbp-reply-header" id="post-271586">
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                  <span class="bbp-reply-post-date">
                   2009年5月31日 上午11:59
                  </span>
                  <span class="bbp-header">
                   回复：
                   <a class="bbp-topic-permalink" href="http://cos.name/cn/topic/15384/">
                    分析结果不一样
                   </a>
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                   4 楼
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                   jhfu
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                  <p>
                   没人帮吗
                  </p>
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                <div class="bbp-reply-header" id="post-271455">
                 <div class="bbp-meta">
                  <span class="bbp-reply-post-date">
                   2009年5月27日 上午2:18
                  </span>
                  <span class="bbp-header">
                   回复：
                   <a class="bbp-topic-permalink" href="http://cos.name/cn/topic/15384/">
                    分析结果不一样
                   </a>
                  </span>
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                   3 楼
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                   jhfu
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                 <div class="bbp-reply-content">
                  <p>
                   书中给出了电视节目评价的例子，用来分析两组变量对节目观点之间的关系
                  </p>
                 </div>
                 <!-- .bbp-reply-content -->
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                <div class="bbp-reply-header" id="post-271317">
                 <div class="bbp-meta">
                  <span class="bbp-reply-post-date">
                   2009年5月25日 上午3:56
                  </span>
                  <span class="bbp-header">
                   回复：
                   <a class="bbp-topic-permalink" href="http://cos.name/cn/topic/15390/">
                    两组样本量悬殊，可以进行成组t检验么？
                   </a>
                  </span>
                  <a class="bbp-reply-permalink" href="http://cos.name/cn/topic/15390/#post-271317">
                   2 楼
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                  <p>
                   可以
                  </p>
                 </div>
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                <div class="bbp-reply-header" id="post-268951">
                 <div class="bbp-meta">
                  <span class="bbp-reply-post-date">
                   2009年4月26日 下午3:25
                  </span>
                  <span class="bbp-header">
                   回复：
                   <a class="bbp-topic-permalink" href="http://cos.name/cn/topic/12451/">
                    老外上中文课的笔记
                   </a>
                  </span>
                  <a class="bbp-reply-permalink" href="http://cos.name/cn/topic/12451/page/3/#post-268951">
                   44 楼
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                  <p>
                   看看
                  </p>
                 </div>
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                <div class="bbp-reply-header" id="post-263193">
                 <div class="bbp-meta">
                  <span class="bbp-reply-post-date">
                   2009年2月7日 上午2:12
                  </span>
                  <span class="bbp-header">
                   回复：
                   <a class="bbp-topic-permalink" href="http://cos.name/cn/topic/13899/">
                    DNA序列聚类
                   </a>
                  </span>
                  <a class="bbp-reply-permalink" href="http://cos.name/cn/topic/13899/#post-263193">
                   2 楼
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                  <p>
                   问题问的模糊，让人无法回答，想帮也帮不了你
                  </p>
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                <div class="bbp-reply-header" id="post-262037">
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                  <span class="bbp-reply-post-date">
                   2009年1月8日 上午10:37
                  </span>
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                   回复：
                   <a class="bbp-topic-permalink" href="http://cos.name/cn/topic/13668/">
                    请帮忙解读结果。
                   </a>
                  </span>
                  <a class="bbp-reply-permalink" href="http://cos.name/cn/topic/13668/#post-262037">
                   6 楼
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                 <div class="bbp-reply-content">
                  <blockquote class="d4pbbc-quote">
                   <p>
                    [b]引用第4楼[i]谢益辉[/i]于[i]2009-01-07 22:57[/i]发表的“”[/b]:
                    <br/>
                    有区别，如果要在回归方程中加入自变量的某种变换（如平方项），则必须用I()“保护”起来。
                    <br/>
                    [code]&gt; x = iris[, 1]
                    <br/>
                    &gt; y = iris[, 2]
                    <br/>
                    &gt; lm(y ~ x)
                   </p>
                   <p>
                    .......
                   </p>
                  </blockquote>
                  <p>
                   明白了，谢谢
                  </p>
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                <div class="bbp-reply-header" id="post-262036">
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                  <span class="bbp-reply-post-date">
                   2009年1月8日 上午10:26
                  </span>
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                   回复：
                   <a class="bbp-topic-permalink" href="http://cos.name/cn/topic/13707/">
                    【求助】 nnet package 结果中weight的问题
                   </a>
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                   3 楼
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                   jhfu
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                 <div class="bbp-reply-content">
                  <p>
                   b是截距项。这个summary相当于给了如下表达式：
                   <br/>
                   h(1)=5.38+8.23x(1)+0.07x(2)-9.15x(3) -7.80x(4)
                   <br/>
                   h(2)=0.32+0.57x(1)+1.80x(2)-2.90x(3) -1.26x(4)
                   <br/>
                   y1= -4.97+10.26h(1) -9.63h(2)
                   <br/>
                   y2= -4.63+0.27h(1)+8.75h(2)
                   <br/>
                   y3=5.16 -10.82h(1) -0.31h(2)
                  </p>
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                <div class="bbp-reply-header" id="post-261947">
                 <div class="bbp-meta">
                  <span class="bbp-reply-post-date">
                   2009年1月7日 上午10:48
                  </span>
                  <span class="bbp-header">
                   回复：
                   <a class="bbp-topic-permalink" href="http://cos.name/cn/topic/13605/">
                    请问统计学里P值如何理解
                   </a>
                  </span>
                  <a class="bbp-reply-permalink" href="http://cos.name/cn/topic/13605/#post-261947">
                   5 楼
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                  </span>
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                <!-- #post-261947 -->
                <div class="odd bbp-parent-forum-990 bbp-parent-topic-13605 bbp-reply-position-5 user-id-58534 post-261947 reply type-reply status-publish hentry">
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                  <p>
                   p值能不能看作是原假设成立的概率呢，这样是不是更加直观易懂呢
                  </p>
                 </div>
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                <!-- .reply -->
                <div class="bbp-reply-header" id="post-261946">
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                  <span class="bbp-reply-post-date">
                   2009年1月7日 上午10:44
                  </span>
                  <span class="bbp-header">
                   回复：
                   <a class="bbp-topic-permalink" href="http://cos.name/cn/topic/13668/">
                    请帮忙解读结果。
                   </a>
                  </span>
                  <a class="bbp-reply-permalink" href="http://cos.name/cn/topic/13668/#post-261946">
                   4 楼
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                  <p>
                   I(rate^2)与rate^2有什么区别啊
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                   2009年1月7日 上午10:40
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                   回复：
                   <a class="bbp-topic-permalink" href="http://cos.name/cn/topic/13668/">
                    请帮忙解读结果。
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                   3 楼
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                    [b]引用第1楼[i]谢益辉[/i]于[i]2009-01-06 20:54[/i]发表的“”[/b]:
                    <br/>
                    I(rate^2)这一项在alpha=0.1的情况下不能舍弃
                   </p>
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                   那么I(rate^2)语句是什么意思呢，这个语句的作用是什么呢
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                   2009年1月7日 上午10:36
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                   回复：
                   <a class="bbp-topic-permalink" href="http://cos.name/cn/topic/13684/">
                    新手求助：用R软件分析收入结构
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                   5 楼
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                   是的，要想让别人帮你，首先帮别人看的轻松一点
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                   2008年12月30日 下午1:38
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                   回复：
                   <a class="bbp-topic-permalink" href="http://cos.name/cn/topic/13571/">
                    初学，请教关于因子的问题
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                   和方差分析中的因素一样
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